
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


!dir

 El volumen de la unidad D es SSD Crucial
 El n£mero de serie del volumen es: E602-7CD2

 Directorio de D:\Code\DSPE\Task LOF

04/16/2021  10:13 PM    <DIR>          .
04/16/2021  10:13 PM    <DIR>          ..
04/07/2021  05:02 PM    <DIR>          .ipynb_checkpoints
04/16/2021  10:13 PM         2,699,210 instructions_without_code_final.ipynb
03/24/2021  05:35 PM            14,075 machine_2021_program_summary_.csv
03/24/2021  05:35 PM               580 tutorial_data.csv
04/07/2021  05:02 PM           736,525 tutorial_during_the_lecture_2021_04_07.ipynb
               4 archivos      3,450,390 bytes
               3 dirs  334,501,134,336 bytes libres

 Volume in drive C is Windows
 Volume Serial Number is 5441-C8EF

 Directory of C:\Users\bialekj\Documents\Projekty\Wyklady\anomaly_detection_workshop\data

19.03.2020  09:06    <DIR>          .
19.03.2020  09:06    <DIR>          ..
19.03.2020  13:28            14˙075 machine_2021_program_summary_.csv
19.03.2020  09:06    <DIR>          old
               1 File(s)         14˙075 bytes
               3 Dir(s)   4˙900˙331˙520 bytes free


df = pd.read_csv('machine_2021_program_summary_.csv')


df.head(10)


df.columns

Index(['start', 'end', 'VibrationPeak_mean [dB]', 'VibrationPeak_median [dB]',
       'VibrationCarpet_mean [dB]', 'VibrationCarpet_median [dB]',
       'Temperature_median [°C]', 'Temperature_mean [°C]', 'program'],
      dtype='object')

Index(['start', 'end', 'VibrationPeak_mean [dB]', 'VibrationPeak_median [dB]',
       'VibrationCarpet_mean [dB]', 'VibrationCarpet_median [dB]',
       'Temperature_median [°C]', 'Temperature_mean [°C]', 'program'],
      dtype='object')


sub1 = df.copy()
for col in df.columns:
    print("Column {} is {}".format(col, df[col].dtype))

Column start is object
Column end is object
Column VibrationPeak_mean [dB] is float64
Column VibrationPeak_median [dB] is float64
Column VibrationCarpet_mean [dB] is float64
Column VibrationCarpet_median [dB] is float64
Column Temperature_median [°C] is float64
Column Temperature_mean [°C] is float64
Column program is object

Column start is object
Column end is object
Column VibrationPeak_mean [dB] is float64
Column VibrationPeak_median [dB] is float64
Column VibrationCarpet_mean [dB] is float64
Column VibrationCarpet_median [dB] is float64
Column Temperature_median [°C] is float64
Column Temperature_mean [°C] is float64
Column program is object


sub1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 89 entries, 0 to 88
Data columns (total 9 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   start                        89 non-null     object 
 1   end                          89 non-null     object 
 2   VibrationPeak_mean [dB]      89 non-null     float64
 3   VibrationPeak_median [dB]    88 non-null     float64
 4   VibrationCarpet_mean [dB]    89 non-null     float64
 5   VibrationCarpet_median [dB]  89 non-null     float64
 6   Temperature_median [°C]      89 non-null     float64
 7   Temperature_mean [°C]        89 non-null     float64
 8   program                      89 non-null     object 
dtypes: float64(6), object(3)
memory usage: 6.4+ KB

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 89 entries, 0 to 88
Data columns (total 9 columns):
start                          89 non-null object
end                            89 non-null object
VibrationPeak_mean [dB]        89 non-null float64
VibrationPeak_median [dB]      88 non-null float64
VibrationCarpet_mean [dB]      89 non-null float64
VibrationCarpet_median [dB]    89 non-null float64
Temperature_median [°C]        89 non-null float64
Temperature_mean [°C]          89 non-null float64
program                        89 non-null object
dtypes: float64(6), object(3)
memory usage: 6.4+ KB


df.isnull().values.any()

True

True


df[df.isnull().values]


len(df[df.isnull().values])/len(df)

0.011235955056179775

0.011235955056179775


sub1.dropna(inplace=True)


sub1 = sub1.reset_index(drop=True)
sub1.head(15)


sub1['start'] = pd.to_datetime(sub1['start'])
sub1['end'] = pd.to_datetime(sub1['end'])


sub1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 88 entries, 0 to 87
Data columns (total 9 columns):
 #   Column                       Non-Null Count  Dtype         
---  ------                       --------------  -----         
 0   start                        88 non-null     datetime64[ns]
 1   end                          88 non-null     datetime64[ns]
 2   VibrationPeak_mean [dB]      88 non-null     float64       
 3   VibrationPeak_median [dB]    88 non-null     float64       
 4   VibrationCarpet_mean [dB]    88 non-null     float64       
 5   VibrationCarpet_median [dB]  88 non-null     float64       
 6   Temperature_median [°C]      88 non-null     float64       
 7   Temperature_mean [°C]        88 non-null     float64       
 8   program                      88 non-null     object        
dtypes: datetime64[ns](2), float64(6), object(1)
memory usage: 6.3+ KB

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 88 entries, 0 to 87
Data columns (total 9 columns):
start                          88 non-null datetime64[ns]
end                            88 non-null datetime64[ns]
VibrationPeak_mean [dB]        88 non-null float64
VibrationPeak_median [dB]      88 non-null float64
VibrationCarpet_mean [dB]      88 non-null float64
VibrationCarpet_median [dB]    88 non-null float64
Temperature_median [°C]        88 non-null float64
Temperature_mean [°C]          88 non-null float64
program                        88 non-null object
dtypes: datetime64[ns](2), float64(6), object(1)
memory usage: 6.3+ KB


sub1.plot(subplots=True, figsize=(10,9), style='o')

array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>,
       <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],
      dtype=object)


sub1.plot(x='start', subplots=True, figsize=(10,9), style='o')

array([<AxesSubplot:xlabel='start'>, <AxesSubplot:xlabel='start'>,
       <AxesSubplot:xlabel='start'>, <AxesSubplot:xlabel='start'>,
       <AxesSubplot:xlabel='start'>, <AxesSubplot:xlabel='start'>,
       <AxesSubplot:xlabel='start'>], dtype=object)


sub2 = sub1[['Temperature_mean [°C]','Temperature_median [°C]','VibrationCarpet_mean [dB]','VibrationCarpet_median [dB]',
            'VibrationPeak_mean [dB]','VibrationPeak_median [dB]']]
sub2.hist(bins=20, figsize=(10,10));


sub1.head(5)


sub1.columns

Index(['start', 'end', 'VibrationPeak_mean [dB]', 'VibrationPeak_median [dB]',
       'VibrationCarpet_mean [dB]', 'VibrationCarpet_median [dB]',
       'Temperature_median [°C]', 'Temperature_mean [°C]', 'program'],
      dtype='object')

Index(['start', 'end', 'VibrationPeak_mean [dB]', 'VibrationPeak_median [dB]',
       'VibrationCarpet_mean [dB]', 'VibrationCarpet_median [dB]',
       'Temperature_median [°C]', 'Temperature_mean [°C]', 'program'],
      dtype='object')


sub3 = sub1[['VibrationPeak_mean [dB]','VibrationPeak_median [dB]',
            'VibrationCarpet_mean [dB]','VibrationCarpet_median [dB]']]
for i,col1 in enumerate(sub3.columns):
    for j,col2 in enumerate(sub3.columns):
            print(i,col1,j,col2)

0 VibrationPeak_mean [dB] 0 VibrationPeak_mean [dB]
0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB]
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB]
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB]
1 VibrationPeak_median [dB] 0 VibrationPeak_mean [dB]
1 VibrationPeak_median [dB] 1 VibrationPeak_median [dB]
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB]
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB]
2 VibrationCarpet_mean [dB] 0 VibrationPeak_mean [dB]
2 VibrationCarpet_mean [dB] 1 VibrationPeak_median [dB]
2 VibrationCarpet_mean [dB] 2 VibrationCarpet_mean [dB]
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB]
3 VibrationCarpet_median [dB] 0 VibrationPeak_mean [dB]
3 VibrationCarpet_median [dB] 1 VibrationPeak_median [dB]
3 VibrationCarpet_median [dB] 2 VibrationCarpet_mean [dB]
3 VibrationCarpet_median [dB] 3 VibrationCarpet_median [dB]


for i,col1 in enumerate(sub3.columns):
    for j,col2 in enumerate(sub3.columns):
        if i != j  and i<j:
            print(col1,col2)

VibrationPeak_mean [dB] VibrationPeak_median [dB]
VibrationPeak_mean [dB] VibrationCarpet_mean [dB]
VibrationPeak_mean [dB] VibrationCarpet_median [dB]
VibrationPeak_median [dB] VibrationCarpet_mean [dB]
VibrationPeak_median [dB] VibrationCarpet_median [dB]
VibrationCarpet_mean [dB] VibrationCarpet_median [dB]

VibrationPeak_mean [dB] VibrationPeak_median [dB]
VibrationPeak_mean [dB] VibrationCarpet_mean [dB]
VibrationPeak_mean [dB] VibrationCarpet_median [dB]
VibrationPeak_median [dB] VibrationCarpet_mean [dB]
VibrationPeak_median [dB] VibrationCarpet_median [dB]
VibrationCarpet_mean [dB] VibrationCarpet_median [dB]


for i,col1 in enumerate(sub3.columns):
    for j,col2 in enumerate(sub3.columns):
        if i != j  and i<j: 
            plt.scatter(sub3[col1],sub3[col2])
            plt.xlabel(col1)
            plt.ylabel(col1)
            plt.title('{} vs {}'.format(col1,col2))
            plt.show()


import seaborn as sns 
for i,col1 in enumerate(sub3.columns):
    for j,col2 in enumerate(sub3.columns):
        if i != j  and i<j:
            sns.jointplot(x=col1, y=col2, data=sub3, marginal_kws={'bins':15});

c:\users\bialekj\appdata\local\programs\python\python36\lib\site-packages\seaborn\distributions.py:218: MatplotlibDeprecationWarning: 
The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.
  color=hist_color, **hist_kws)


for i,col1 in enumerate(sub3.columns):
    for j,col2 in enumerate(sub3.columns):
        if i != j  and i<j:
            sns.jointplot(x=col1, y=col2, data=sub3, kind='hex', joint_kws={'gridsize':12}, marginal_kws={'bins':15});


sub1.value_counts('program')

program
A11    32
A12    29
A13    27
dtype: int64

A11    32
A12    29
A13    27
Name: program, dtype: int64


sub4 = sub1[['VibrationPeak_mean [dB]','VibrationPeak_median [dB]',
            'VibrationCarpet_mean [dB]','VibrationCarpet_median [dB]','program']]
for i,col1 in enumerate(sub4.columns):
    for j,col2 in enumerate(sub4.columns):
        for k,col3 in enumerate(sub4.columns):
            if i!=j  and i<j and j!=4:
                 print(i,col1,j,col2,k,col3)

0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB] 0 VibrationPeak_mean [dB]
0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB] 1 VibrationPeak_median [dB]
0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB]
0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB]
0 VibrationPeak_mean [dB] 1 VibrationPeak_median [dB] 4 program
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB] 0 VibrationPeak_mean [dB]
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB] 1 VibrationPeak_median [dB]
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB] 2 VibrationCarpet_mean [dB]
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB]
0 VibrationPeak_mean [dB] 2 VibrationCarpet_mean [dB] 4 program
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB] 0 VibrationPeak_mean [dB]
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB] 1 VibrationPeak_median [dB]
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB] 2 VibrationCarpet_mean [dB]
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB] 3 VibrationCarpet_median [dB]
0 VibrationPeak_mean [dB] 3 VibrationCarpet_median [dB] 4 program
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB] 0 VibrationPeak_mean [dB]
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB] 1 VibrationPeak_median [dB]
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB] 2 VibrationCarpet_mean [dB]
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB]
1 VibrationPeak_median [dB] 2 VibrationCarpet_mean [dB] 4 program
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB] 0 VibrationPeak_mean [dB]
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB] 1 VibrationPeak_median [dB]
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB] 2 VibrationCarpet_mean [dB]
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB] 3 VibrationCarpet_median [dB]
1 VibrationPeak_median [dB] 3 VibrationCarpet_median [dB] 4 program
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB] 0 VibrationPeak_mean [dB]
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB] 1 VibrationPeak_median [dB]
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB] 2 VibrationCarpet_mean [dB]
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB] 3 VibrationCarpet_median [dB]
2 VibrationCarpet_mean [dB] 3 VibrationCarpet_median [dB] 4 program


for i,col1 in enumerate(sub4.columns):
    for j,col2 in enumerate(sub4.columns):
        for k,col3 in enumerate(sub4.columns):
            if i!=j and i<j and j!=4 and k==4:
                plt.figure(figsize=(10,10))
                sns.scatterplot(x=col1, y=col2, data=sub4, hue="program", hue_order=["A11","A12","A13"], s=150)
                plt.title('{} vs {}'.format(col1,col2))
                plt.show()


sub5 = sub1.copy()
sub5.loc[sub5['program'] == 'A11','program'] = 1
sub5.loc[sub5['program'] == 'A12','program'] = 3
sub5.loc[sub5['program'] == 'A13','program'] = 2
sub5


 sub5.head(5)


sub6 = sub5[['VibrationPeak_mean [dB]','VibrationPeak_median [dB]',
            'VibrationCarpet_mean [dB]','VibrationCarpet_median [dB]','program']]
for i,col1 in enumerate(sub6.columns):
    for j,col2 in enumerate(sub6.columns):
        for k,col3 in enumerate(sub6.columns):
            if i!=j  and i<j and k==4:
                plt.figure(figsize=(10,10))
                sns.scatterplot(x=col1, y=col2, data=sub6, hue="program", palette=['green','blue','red'], s=150)
                plt.title('{} vs {}'.format(col1,col2))
                plt.show()


from sklearn.neighbors import LocalOutlierFactor


sub6


lof2 = LocalOutlierFactor(n_neighbors=10, contamination='auto', novelty=False)


lof.fit(sub6)

LocalOutlierFactor(n_neighbors=10)


lof.negative_outlier_factor_

array([-1.05663666, -1.12918115, -0.96358471, -1.36992817, -1.43083105,
       -1.00562152, -1.06542242, -1.6609807 , -2.152835  , -1.12830735,
       -1.12208118, -0.93529643, -3.46487282, -1.17208657, -1.03261805,
       -1.05794032, -1.10804777, -0.9600572 , -0.9668995 , -1.16045169,
       -0.96417508, -1.097853  , -1.04124847, -0.96358053, -1.02410361,
       -1.17417803, -1.03971873, -1.23648819, -1.04579194, -1.01311484,
       -0.97781779, -1.1398881 , -1.25883338, -0.98298584, -0.97897699,
       -1.01705972, -1.46264757, -1.31281301, -1.00288781, -0.9928458 ,
       -1.00784891, -0.97427814, -1.0490519 , -1.28509574, -2.02352473,
       -0.95705191, -0.98509853, -1.07312896, -1.03774076, -1.02430962,
       -1.04183713, -0.98668067, -0.99249329, -0.96142413, -0.97004489,
       -1.0390082 , -0.97559897, -1.21981806, -1.28802528, -1.04573126,
       -0.98420793, -0.959407  , -1.15309822, -1.2082514 , -1.06992494,
       -4.73278711, -1.18517409, -1.0245172 , -0.94253738, -1.21543405,
       -1.0200107 , -0.97440239, -6.65200629, -0.99785117, -1.13296133,
       -0.99068261, -1.53316828, -1.6010427 , -0.98249186, -1.06264464,
       -0.9744652 , -1.84780867, -1.01940412, -1.15547654, -1.15645655,
       -1.1209191 , -1.20875806, -0.98146582])


sub6_results = sub6.copy()
neg_values = lof.negative_outlier_factor_
sub6_results['negative_lof'] = neg_values
sub6_results


for i,col1 in enumerate(sub6_results.columns):
    for j,col2 in enumerate(sub6_results.columns):
        for k,col3 in enumerate(sub6_results.columns):
            if i!=j  and i<j and j!=5 and k==4:
                plt.figure(figsize=(10,10))
                sns.scatterplot(x=col1, y=col2, data=sub6_results, hue="negative_lof", palette="rocket", 
                                edgecolor="black", s=150)
                plt.title('{} vs {}'.format(col1,col2))
                plt.show()


sub7 = sub6.copy()
lof2 = LocalOutlierFactor(n_neighbors=10, contamination='auto', novelty=False)
lof2.fit(sub7)
lof2.negative_outlier_factor_
sub7_results = sub7.copy()
neg_values2 = lof2.negative_outlier_factor_
sub7_results['negative_lof'] = neg_values2


 for i,col1 in enumerate(sub7_results.columns):
    for j,col2 in enumerate(sub7_results.columns):
        for k,col3 in enumerate(sub7_results.columns):
            if i!=j  and i<j and j!=5 and k==4:
                plt.figure(figsize=(10,10))
                sns.scatterplot(x=col1, y=col2, data=sub7_results, hue="negative_lof", palette="rocket",
                                style="program", edgecolor="black", s=150)
                plt.title('{} vs {}'.format(col1,col2))
                plt.show()
#For me, assigning a shape to each program makes it easier to visualize outliers. (complementing what LOF can't detect)

	start	end	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	Temperature_median [°C]	Temperature_mean [°C]	program
0	2019-09-18 18:58:53	2019-09-18 19:18:53	-6.921236	-8.304498	-8.264868	-5.481411	-21.700001	-21.700001	A11
1	2019-09-18 19:18:53	2019-09-18 19:38:53	-6.470144	-7.244804	-2.106002	-10.807974	21.700001	21.700001	A13
2	2019-09-19 09:54:53	2019-09-19 10:14:53	34.503162	35.762915	27.908133	22.816784	-22.299999	-22.299999	A13
3	2019-09-19 10:14:53	2019-09-19 10:34:53	22.144701	36.509675	28.524839	28.941813	22.200001	22.200001	A13
4	2019-09-19 10:34:53	2019-09-19 10:54:53	-5.823500	-4.848217	-1.193132	2.436969	-22.100000	-22.100000	A11
5	2019-09-19 20:03:51	2019-09-19 20:23:51	-2.962862	-7.298702	-4.637185	-3.323306	21.500000	21.500000	A11
6	2019-09-21 04:03:55	2019-09-21 04:23:55	30.678012	35.091929	21.354449	28.506288	-21.900000	-21.900000	A13
7	2019-09-26 17:20:48	2019-09-26 17:40:48	32.561561	32.761023	19.731399	18.239473	22.100000	22.100000	A13
8	2019-09-26 17:40:48	2019-09-26 18:00:48	20.310017	38.026277	13.001814	22.507591	-22.200001	-22.200001	A13
9	2019-09-27 11:16:58	2019-09-27 11:36:58	-5.022353	-7.529789	-10.443645	-9.293475	23.400000	23.400000	A11

	start	end	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	Temperature_median [°C]	Temperature_mean [°C]	program
0	2019-09-18 18:58:53	2019-09-18 19:18:53	-6.921236	-8.304498	-8.264868	-5.481411	-21.700001	-21.700001	A11
1	2019-09-18 19:18:53	2019-09-18 19:38:53	-6.470144	-7.244804	-2.106002	-10.807974	21.700001	21.700001	A13
2	2019-09-19 09:54:53	2019-09-19 10:14:53	34.503162	35.762915	27.908133	22.816784	-22.299999	-22.299999	A13
3	2019-09-19 10:14:53	2019-09-19 10:34:53	22.144701	36.509675	28.524839	28.941813	22.200001	22.200001	A13
4	2019-09-19 10:34:53	2019-09-19 10:54:53	-5.823500	-4.848217	-1.193132	2.436969	-22.100000	-22.100000	A11
5	2019-09-19 20:03:51	2019-09-19 20:23:51	-2.962862	-7.298702	-4.637185	-3.323306	21.500000	21.500000	A11
6	2019-09-21 04:03:55	2019-09-21 04:23:55	30.678012	35.091929	21.354449	28.506288	-21.900000	-21.900000	A13
7	2019-09-26 17:20:48	2019-09-26 17:40:48	32.561561	32.761023	19.731399	18.239473	22.100000	22.100000	A13
8	2019-09-26 17:40:48	2019-09-26 18:00:48	20.310017	38.026277	13.001814	22.507591	-22.200001	-22.200001	A13
9	2019-09-27 11:16:58	2019-09-27 11:36:58	-5.022353	-7.529789	-10.443645	-9.293475	23.400000	23.400000	A11

	start	end	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	Temperature_median [°C]	Temperature_mean [°C]	program
0	2019-09-18 18:58:53	2019-09-18 19:18:53	-6.921236	-8.304498	-8.264868	-5.481411	-21.700001	-21.700001	A11
1	2019-09-18 19:18:53	2019-09-18 19:38:53	-6.470144	-7.244804	-2.106002	-10.807974	21.700001	21.700001	A13
2	2019-09-19 09:54:53	2019-09-19 10:14:53	34.503162	35.762915	27.908133	22.816784	-22.299999	-22.299999	A13
3	2019-09-19 10:14:53	2019-09-19 10:34:53	22.144701	36.509675	28.524839	28.941813	22.200001	22.200001	A13
4	2019-09-19 10:34:53	2019-09-19 10:54:53	-5.823500	-4.848217	-1.193132	2.436969	-22.100000	-22.100000	A11
5	2019-09-19 20:03:51	2019-09-19 20:23:51	-2.962862	-7.298702	-4.637185	-3.323306	21.500000	21.500000	A11
6	2019-09-21 04:03:55	2019-09-21 04:23:55	30.678012	35.091929	21.354449	28.506288	-21.900000	-21.900000	A13
7	2019-09-26 17:20:48	2019-09-26 17:40:48	32.561561	32.761023	19.731399	18.239473	22.100000	22.100000	A13
8	2019-09-26 17:40:48	2019-09-26 18:00:48	20.310017	38.026277	13.001814	22.507591	-22.200001	-22.200001	A13
9	2019-09-27 11:16:58	2019-09-27 11:36:58	-5.022353	-7.529789	-10.443645	-9.293475	23.400000	23.400000	A11
10	2019-09-27 11:36:58	2019-09-27 11:56:58	-8.220528	-5.825943	-7.020347	-9.362177	-23.400000	-23.400000	A11
11	2019-09-27 17:24:01	2019-09-27 17:44:01	-6.506379	-10.573105	-3.227738	-6.040868	23.000000	23.000000	A11
12	2019-10-01 18:44:17	2019-10-01 19:04:17	9.137873	15.062634	1.620201	3.346457	23.700001	23.700001	A12
13	2019-10-03 18:19:38	2019-10-03 18:24:43	17.853521	18.031566	16.722668	11.515177	-22.400000	-22.400000	A12
14	2019-10-03 18:24:46	2019-10-03 18:44:46	20.420528	18.664966	11.114553	15.407386	22.400000	22.400000	A12

	start	end	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	Temperature_median [°C]	Temperature_mean [°C]	program
0	2019-09-18 18:58:53	2019-09-18 19:18:53	-6.921236	-8.304498	-8.264868	-5.481411	-21.700001	-21.700001	A11
1	2019-09-18 19:18:53	2019-09-18 19:38:53	-6.470144	-7.244804	-2.106002	-10.807974	21.700001	21.700001	A13
2	2019-09-19 09:54:53	2019-09-19 10:14:53	34.503162	35.762915	27.908133	22.816784	-22.299999	-22.299999	A13
3	2019-09-19 10:14:53	2019-09-19 10:34:53	22.144701	36.509675	28.524839	28.941813	22.200001	22.200001	A13
4	2019-09-19 10:34:53	2019-09-19 10:54:53	-5.823500	-4.848217	-1.193132	2.436969	-22.100000	-22.100000	A11
5	2019-09-19 20:03:51	2019-09-19 20:23:51	-2.962862	-7.298702	-4.637185	-3.323306	21.500000	21.500000	A11
6	2019-09-21 04:03:55	2019-09-21 04:23:55	30.678012	35.091929	21.354449	28.506288	-21.900000	-21.900000	A13
7	2019-09-26 17:20:48	2019-09-26 17:40:48	32.561561	32.761023	19.731399	18.239473	22.100000	22.100000	A13
8	2019-09-26 17:40:48	2019-09-26 18:00:48	20.310017	38.026277	13.001814	22.507591	-22.200001	-22.200001	A13
9	2019-09-27 11:16:58	2019-09-27 11:36:58	-5.022353	-7.529789	-10.443645	-9.293475	23.400000	23.400000	A11
10	2019-09-27 11:36:58	2019-09-27 11:56:58	-8.220528	-5.825943	-7.020347	-9.362177	-23.400000	-23.400000	A11
11	2019-09-27 17:24:01	2019-09-27 17:44:01	-6.506379	-10.573105	-3.227738	-6.040868	23.000000	23.000000	A11
12	2019-10-01 18:44:17	2019-10-01 19:04:17	9.137873	15.062634	1.620201	3.346457	23.700001	23.700001	A12
13	2019-10-03 18:19:38	2019-10-03 18:24:43	17.853521	18.031566	16.722668	11.515177	-22.400000	-22.400000	A12
14	2019-10-03 18:24:46	2019-10-03 18:44:46	20.420528	18.664966	11.114553	15.407386	22.400000	22.400000	A12

	start	end	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	Temperature_median [°C]	Temperature_mean [°C]	program
0	2019-09-18 18:58:53	2019-09-18 19:18:53	-6.921236	-8.304498	-8.264868	-5.481411	-21.700001	-21.700001	A11
1	2019-09-18 19:18:53	2019-09-18 19:38:53	-6.470144	-7.244804	-2.106002	-10.807974	21.700001	21.700001	A13
2	2019-09-19 09:54:53	2019-09-19 10:14:53	34.503162	35.762915	27.908133	22.816784	-22.299999	-22.299999	A13
3	2019-09-19 10:14:53	2019-09-19 10:34:53	22.144701	36.509675	28.524839	28.941813	22.200001	22.200001	A13
4	2019-09-19 10:34:53	2019-09-19 10:54:53	-5.823500	-4.848217	-1.193132	2.436969	-22.100000	-22.100000	A11

	VibrationPeak_mean [dB]	VibrationPeak_median [dB]	VibrationCarpet_mean [dB]	VibrationCarpet_median [dB]	program	negative_lof
0	-6.921236	-8.304498	-8.264868	-5.481411	1	-1.056637
1	-6.470144	-7.244804	-2.106002	-10.807974	2	-1.129181
2	34.503162	35.762915	27.908133	22.816784	2	-0.963585
3	22.144701	36.509675	28.524839	28.941813	2	-1.369928
4	-5.823500	-4.848217	-1.193132	2.436969	1	-1.430831
...	...	...	...	...	...	...
83	-5.548868	-6.355704	-10.301523	-5.439592	1	-1.155477
84	-5.811831	-4.334031	-0.468306	-8.513778	1	-1.156457
85	-1.294405	-7.711131	-5.414103	-3.745550	1	-1.120919
86	-5.099513	-8.520697	-13.016096	-7.445794	1	-1.208758
87	-2.897968	-6.233262	-4.108349	-4.903356	1	-0.981466